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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Causal Structure learning from Imperfect Longitudinal data in Healthcare 11

Causal Structure Learning from Imperfect Longitudinal Data i...

引用

11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Yang, Haoyu Tourani, Roshan Li, Jia Caraballo, Pedro Steinbach, Michael Kumar, Vipin Simon, Gyorgy University of Minnesota Dept. of Computer Science and Engineering Minneapolis United States University of Minnesota Institute for Health Informatics Minneapolis United States Mayo Clinic Dept. of Internal Medicine Rochester United States

ISBN: (纸本)9798350302639

Causal Structure Discovery (CSD) is the task of learning the set of underlying causal relationships from observational data. Due to their computational scalability and flexibility, a recently developed class of CSD methods, NOTEARS, based on a formulation allowing for continuous optimization, is gaining popularity. However, this formulation can lead to incorrect edge orientations when little/no likelihood advantage is conferred upon any edge orientation, e.g., a → b → c versus c → b → a. In longitudinal data, like electronic health records (EHRs), temporal relationships are observable among many pairs of variables. Such temporal relationship is imperfect but still suggest an orientation since causal effects cannot travel backwards in time. Following this idea, we propose methods to incorporate precedence constraints into continuous optimization-based CSD methods. Experiments on both a synthetic and two real-world datasets validate the effectiveness of the proposed precedence constraints. © 2023 IEEE.

关键词： Machine learning

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CBSB: Robust Cancelable Biometric System for Banks Using Deep learning 11

CBSB: Robust Cancelable Biometric System for Banks Using Dee...

引用

11th international Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Sayed, Bassel Yasser Mahmoud, Amel Mohamed El-Rabaie, El-Sayed Bauomy, Nariman Abdel-Salam Faculty of Engineering Communications and Electronics Engineering Department Cairo Egypt Faculty of Engineering Communications and Electronics Engineering Department Giza Egypt Menoufia University Faculty of Electronic Engineering Electronics and Communications Department Menoufia Egypt

ISBN: (纸本)9798350369328

Biometric authentication is a reliable and convenient technique for confirming a person's identity by analyzing their distinct physiological or behavioral traits. While biometric authentication offers several benefits compared to conventional methods, it confronts challenges related to privacy and security. To address these concerns, cancelable biometric systems (CBS) convert biometric data into non-invertible templates, allowing for revocation and re-issuance if security is compromised. CBS also enhances diversity and renewability in authentication solutions. In this paper, we introduce CSBS: A robust deep-learning-based framework for cancelable biometric systems for banks. the system extracts distinct characteristics from fingerprint and facial images and subsequently transforms them into revocable templates through the Format-Preserving Encryption (FPE) methodology. these consolidated templates are securely archived within a database server, employing the Bloom filter data structure, and we compare them during the biometric authentication process using the Cosine similarity measure. We apply different deep learning models, such as VGG-16 and LeNet-LSTM, to extract features from the biometric images and achieve the best accuracy rates of 98.85% for face and 99.36% for fingerprint recognition respectively. the experimental results show that the system can generate efficient cancelable biometric templates that can be used for banking authentication without compromising the original biometric data. © 2023 IEEE.

关键词： Authentication

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Technical Research and Development Suggestions of New Generation intelligent Satellite Network System 11th

Technical Research and Development Suggestions of New Genera...

引用

11th international conference on Signal and Information Processing, Network and Computers, ICSINC 2023

作者： Zuo, Bo Shao, Zhuang Ma, Jian Huang, Yufei Institute of Remote Sensing Satellite CAST Beijing100094 China

ISBN: (纸本)9789819721238

With the depth integration of new generation of information technology and space technology, the satellite system will undergo subversive changes and is developing in the direction of systematization, networking and intelligence. the thesis analyzes the development status of intelligent satellite network system, analyzes the development needs to accelerate the system construction, intelligent application and manufacturing mode transformation, and discusses the general characteristics of the new-generation intelligent satellite network system, which is transforming from a single satellite to a satellite system, from acquiring data to acquiring knowledge, and from a general electromechanical system to an intelligent system. the paper proposes an intelligent information system centered on high-performance computers, universal operating systems and intelligent application algorithms, studies key technologies such as task planning, intelligent processing and network interconnection for accelerating the development of the intelligent satellite network system, and gives the medium- and long-term development routes, key indexes and general recommendations for the new generation intelligent satellite network system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellites

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Developing a Labeled dataset for Chili Plant Health Monitoring: A Multispectral Image Segmentation Approach with YOLOv8 11

Developing a Labeled Dataset for Chili Plant Health Monitori...

引用

11th international conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Laksamana, Aditia Dika Putra Fakhrurroja, Hanif Pramesti, Dita School of Industrial Engineering Telkom University Bandung Indonesia School of Industrial Engineering Telkom University National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

this research explores a new method for assessing chili plant health using multispectral camera imagery and deep learning-based image segmentation. data from Bale Tatanen Universitas Padjadjaran chili farms were used to create a labeled dataset with four health categories. Initial image processing involved FastSAM inference to generate binary masks, followed by training a YOLOv8 model for improved segmentation accuracy. this model enabled NDVI calculation and automatic health labeling in unseen images, contributing to automated chili plant health monitoring systems. Evaluation showed an average Dice Coefficient of 63.80%, indicating moderate overlap between predicted and true masks. High precision and robust segmentation across various conditions were observed, though improvements are needed in fine detail segmentation. this approach enhances understanding of chili plant health and supports further studies in the field. © 2024 IEEE.

关键词： Image segmentation

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Pheromone Matrix Eigenvalue-based Convergence Assessment Indicator for Ant Colony Optimization 11

Pheromone Matrix Eigenvalue-based Convergence Assessment Ind...

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11th international conference on Machine Intelligence theory and Applications, MiTA 2024

作者： Li, Xu Shi, Lin Li, Jian-Yu Xu, Jing Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong University of Finance and Economics Research Center of Intelligent Computing & Big Data Technology Guangdong Guangzhou510320 China Nankai University College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9798350385670

As a crucial member of the evolutionary computation (EC), ant colony optimization (ACO) converges to the optimal result through a continuous process of iterative optimization. However, determining the convergence status of an ant colony remains a challenging task without well-established methods. To this end, this paper proposes a global evaluation indicator - average absolute eigenvalue of pheromone matrix (AAEPM) by constructing a pheromone matrix and analyzing its eigenvalue variations. this indicator aims to assess the convergence status of the ant colony during the evolutionary process. Experimental results show that the AAEPM is correlated with the convergence status of ACO and is not significantly influenced by algorithm parameters and problem scale. © 2024 IEEE.

关键词： Anthropogenic

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Smart Conversations: Enhancing User Engagement through NLP in IoT Environments 11

Smart Conversations: Enhancing User Engagement through NLP i...

引用

11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Muthugurunathan, G. Padmapriya, S. Leelavathy, L. Talukdar, Veera Gupta, Ankur Mittal, Manisha Madanapalle Institute of Technology & Science Dept. of Cse Andhra Pradesh India Srm Trichy Arts and Science College Dept. of Computer Science TN Irungalur India D y Patil International University Dept.of Cs Akurdi Maharashtra Pune India Vaish College of Engineering Dept.of Cse Haryana Rohtak India Guru Tegh Bahadur Institute of Technology Ggsipu Dept. of Ece New Delhi India

ISBN: (纸本)9798350350357

the integration of NLP into IoT settings has emerged as a revolutionary framework, providing consumers with a more intuitive and participatory experience. this study examines the capacity of NLP to improve user involvement in IoT ecosystems by facilitating intelligent interactions between people and linked objects. the exponential growth of IoT devices has resulted in a surge of data, necessitating the development of efficient and user-friendly interfaces to facilitate smooth interactions. NLP is crucial in connecting people and IoT devices by enabling the comprehension and production of language that resembles human communication. By adding NLP, IoT environments may give users with a conversational interface, allowing them to manage and monitor equipment with natural language instructions. this study focuses on doing a comprehensive examination of current NLP approaches and their suitability for use in IoT environments. this work examines the problems and potential of integrating NLP in various IoT situations. It specifically focuses on tackling concerns related to language variety, contextual comprehension, and the need for real-time processing. Moreover, the research examines the influence of intelligent dialogues on user involvement, highlighting the possibility of heightened user contentment, effectiveness, and availability. Case studies and practical examples demonstrate effective use of NLP in IoT applications. © 2024 IEEE.

关键词： Natural language processing systems

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Improving Visual Path Odometry for Smart Autonomous E-Bikes 11

Improving Visual Path Odometry for Smart Autonomous E-Bikes

引用

11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Mohamed, Basil Soubra, Hassan Mazhr, Ahmed German University in Cairo Computer Science and Engineering GUC Cairo Egypt Ecole Centrale d'Élctronique Computer Engineering ECE Lyon France

ISBN: (纸本)9798350322101

Accurate global localization is very crucial in Autonomous Road vehicles. Visual Odometry (VO) uses data obtained from the movement of the vehicle and camera images to apply image processing and computer vision techniques to estimate the trajectory of the vehicle. VO can also be fused with other odometry systems such as LiDAR-based odometry to try to enhance the accuracy of the algorithm. this paper introduces innovative techniques and proposes a system to improve path odometry for smart autonomous E-bikes. Firstly, we present an image pre-processing technique using VGG16, a deep convolutional neural network, which enhances feature extraction and tracking. Secondly, we propose a method for generating optical flow reference points using the Shi-Tomasi corner detection algorithm, improving motion estimation in challenging scenarios. Lastly, we implement eight monocular visual odometry (MVO) algorithms on a smart autonomous E-bike prototype, comprising four feature-based and four appearance-based MVOs. © 2023 IEEE.

关键词： Motion estimation

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TABCAPS: A CAPSULE NEURAL NETWORK FOR TABULAR data CLASSIFICATION WIth BOW ROUTING 11

TABCAPS: A CAPSULE NEURAL NETWORK FOR TABULAR DATA CLASSIFIC...

引用

11th international conference on learning Representations, ICLR 2023

作者： Chen, Jintai Liao, Kuanlun Fang, Yanwen Chen, Danny Z. Wu, Jian College of Computer Science and Technology Zhejiang University China Department of Statistics & Actuarial Science The University of Hong Kong Hong Kong Department of Computer Science and Engineering University of Notre Dame United States Department of Public Health Zhejiang University School of Medicine China

Records in a table are represented by a collection of heterogeneous scalar features. Previous work often made predictions for records in a paradigm that processed each feature as an operating unit, which requires to well cope with the heterogeneity. In this paper, we propose to encapsulate all feature values of a record into vectorial features and process them collectively rather than have to deal with individual ones, which directly captures the representations at the data level and benefits robust performances. Specifically, we adopt the concept of capsules to organize features into vectorial features, and devise a novel capsule neural network called TABCAPS to process the vectorial features for classification. In TABCAPS, a record is encoded into several vectorial features by some optimizable multivariate Gaussian kernels in the primary capsule layer, where each vectorial feature represents a specific profile of the input record and is transformed into senior capsule layer under the guidance of a new straightforward routing algorithm. the design of routing algorithm is motivated by the Bag-of-Words (BoW) model, which performs capsule feature grouping straightforwardly and efficiently, in lieu of the computationally complex clustering of previous routing algorithms. Comprehensive experiments show that TABCAPS achieves competitive and robust performances in tabular data classification tasks. Codes are available at https://***/WhatAShot/TabCaps. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Classification (of information)

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TIER BALANCING: TOWARDS DYNAMIC FAIRNESS OVER UNDERLYING CAUSAL FACTORS 11

TIER BALANCING: TOWARDS DYNAMIC FAIRNESS OVER UNDERLYING CAU...

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11th international conference on learning Representations, ICLR 2023

作者： Tang, Zeyu Chen, Yatong Liu, Yang Zhang, Kun Department of Philosophy Carnegie Mellon University United States Computer Science and Engineering Department University of California Santa Cruz United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

the pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Decision making

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the Potential of AI in Electrical and Electronic engineering Education: A Review

The Potential of AI in Electrical and Electronic Engineering...

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IEEE international conference on E-learning in Industrial Electronics, ICELIE

作者： Jiaqin Sun Chiew-Foong Kwong Giampaolo Buticchi Department of Electrical and Electronic Engineering University of Nottingham Ningbo China

ISBN: (数字)9798350363654

ISBN: (纸本)9798350363661

the rapid advancement of Artificial Intelligence (AI) technologies is transforming education, particularly in Electrical and Electronic engineering (EEE). this paper explores the potential applications, benefits, and challenges of Generative AI (GenAI) and Large Language Models (LLMs) in EEE education. Key areas include personalized learning, intelligent tutoring systems, automated grading, and predictive analytics. While these technologies offer significant enhancements in teaching and learning, they also present challenges such as data privacy, bias, and the need for human interaction. By examining current implementations and providing recommendations, this paper aims to guide educators and researchers in effectively integrating AI to improve EEE education.

关键词： Industrial electronics Electric potential data privacy Electronic learning Reviews Generative AI Large language models Education Predictive analytics Electronics engineering education

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